Speech understanding is a growing field that involves enabling a machine to interpret the meaning of spoken language. One aspect of speech understanding is spoken utterance classification, which seeks to apply semantic classification to spoken utterances. Spoken utterance classification typically involves a two-step process. First, a spoken utterance is processed with techniques of automatic speech recognition, using a language model, to determine the sequence of words expressed in the spoken utterance. Second, the sequence of words is processed with techniques of semantic classification, using a classification model, to parse their meaning.
Each of these two steps has the potential to work imperfectly and produce erroneous results. Since the output from the speech recognition process forms the input to the semantic classification process, this means any erroneous results of the speech recognition process will be perpetuated in the semantic classification process, reflecting the old computing aphorism of “garbage in, garbage out”. The risk of erroneous final output from the semantic classification process is therefore compounded. The potential for error has typically been addressed by training the language model to reduce errors in determining the sequences of words from spoken utterances, and training the classification model to reduce errors in determining the semantic classes of the word sequences.
The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.